What are the clustering algorithms used for data mining?

This agglutination hierarchical clustering algorithm: calculate the proximity matrix repeat merge the two closest families to update the proximity matrix until only one family remains, much like Huffman's algorithm, when calculating the proximity of the family, there can be min, max, group average, distance between centroids, etc. (meaning as the name suggests), different proximity measures may produce different results. There are also their own advantages and disadvantages, such as min will be sensitive to noise or outlier...

Disadvantages: high time complexity, o(m 3), the improved algorithm also has o(m 2lgm), m is the number of points; The shortcomings of greedy algorithms, one wrong step and one wrong step; Same as k-means,difficulty handling different sized clusters and convex shapesAdvantages: good interpretability (e.g. when you need to create a taxonomy); Some studies have shown that these algorithms can produce high-quality clustering, and they will also be applied to the merging stage after taking the k-means with the larger k mentioned above. There are also non-spherical families that cannot be solved by k-means.

The statistics teacher talked about some traditional clustering methods, which belong to the category of systematic clustering, first define the distance between observations and the distance calculation method between classes, and then merge the two closest observations (classes) according to the distance until they are merged into one large class. Shortest distance method: The class spacing is the distance that is the closest observed in the two classes.

It does not limit the shape of the class, which has a good effect on the elongated distribution, and deletes the longest distance method of the observation point at the edge: the class spacing is the farthest observed distance between the two classes. It tends to produce classes of equal diameter, which are susceptible to outliers.

Intermediate distance method: The class spacing is weighted by the longest distance, the shortest distance, and the intra-class distance. Center of gravity method:

Class spacing is the distance between two classes of centroids robust to singular values, class averaging: class spacing is the average of the distance between two types of observations. Classes with small variances tend to be merged first, favoring classes that produce the same variance.

Dispersion sum of squares: Merging the two classes with the smallest intra-class variance after merging tends to produce two classes of equal numbers, sensitive to outliers, and density estimation: the longer distance is set to infinity.

For the two samples that are closer, the distance is inversely proportional to the local density. It is suitable for irregular shape classes, and it is not suitable for the number of samples is too small. Two-stage density estimation:

The distance was calculated by density estimation, and then clustered by the shortest distance method. It is more universal. <>

Classification is a very important task in data mining, which can extract a function or model (also commonly called a classifier) that describes a data class from a dataset and attribute each object in the dataset to a known object class. From the perspective of machine learning, classification technology is a kind of guided learning, that is, the number of each training sample has a class identifier before the collapse of the object, and the corresponding knowledge between the data object and the class identifier can be formed through learning. In this sense, the goal of data mining is to classify the source data based on the class knowledge formed by the sample data, and then it can also classify the future data.

Classification has a wide range of applications, such as medical diagnosis, credit grading of credit cards, image pattern recognition.

Unlike classification techniques, clustering is a type of unguided learning in machine learning. In other words, clustering is a method of clustering information according to the principle of information similarity without knowing the class to be divided in advance. The purpose of clustering is to make the differences between objects belonging to the same category as small as possible, and the differences between objects in different categories as large as possible.

Therefore, the significance of clustering is to organize the observed content into a hierarchical structure, to organize similar things together. Clustering allows one to identify dense and sparse areas, and thus global distribution patterns, as well as interesting relationships between data attributes.

Data clustering is a booming field. Clustering techniques are mainly based on statistical methods, machine learning, neural networks, and other methods. The most representative clustering techniques are clustering methods based on geometric distances, such as Euclidean distance, Manhattan distance, Minkowski distance, etc.

Cluster analysis is widely used in a variety of fields such as business, biology, geography, and network services.

The main calculation methods of cluster analysis are: hierarchical method, partitioning method, density-based method, grid-based method, model-based method, etc. Among them, the first two algorithms are measured using statistically defined distances.

The working process of the k-means algorithm is described as follows: firstly, k objects are arbitrarily selected from n data objects as the initial clustering center; For the remaining objects, they are assigned to the cluster that is most similar to them (the cluster center represents) according to their similarity (distance) from these cluster centers; The cluster center (the average of all objects in the cluster) is then calculated for each new cluster; This process is repeated until the standard measure function begins to converge. In general, the mean square deviation is used as a standard measure function.

k clusters have the following characteristics: the clusters themselves are as compact as possible, and the clusters are as separate as possible.

The process is as follows:

1) Arbitrarily select k objects from n data objects as the initial clustering center;

2) calculate the distance of each object from these center objects based on the mean value of each clustered object (center object); and re-divide the corresponding objects according to the minimum distance;

3) recalculate the mean (central object) for each (with variation) cluster;

4) loops (2) and (3) until each cluster no longer changes (convergence of standard measurement functions).

Advantages: The square error of k divisions determined by this algorithm is the smallest. The effect is better when the clustering is dense and the distinction between classes is obvious.

For processing large datasets, this algorithm is relatively scalable and efficient, and the computational complexity is o(nkt), where n is the number of data objects and t is the number of iterations.

Cons: 1k is given in advance, but it is very difficult to choose;

2.The selection of the initial clustering center has a great impact on the clustering results.

Data clustering is an unsupervised approach to machine learning. The data clustering algorithm can be divided into two types of algorithms: structural or dispersed, and in terms of calculation methods, it can be divided into two calculation methods: top-down (large-small, overall to concrete) and bottom-up (small-large, specific to the whole).

Systematic clustering, also known as hierarchical clustering, is to cluster samples that are closer to each other into a class first, and samples that are far away are clustered into classes later, and finally each sample can find a suitable cluster by continuously calculating the distance between samples.

From the process analysis of clustering, clustering can be divided into:

1. Systematic clustering: It is mainly used for inter-sample clustering and index clustering of small data volumes.

2. Stepwise clustering method: also known as fast clustering method, it is mainly used for clustering between big data samples.

3. Ordinal sample clustering method: a vertical and closed method used to cluster ordered data samples and cluster adjacent samples into one class.

4. Fuzzy clustering method: a sample clustering analysis method based on fuzzy mathematics, which is mainly suitable for small data samples.

In clustering, the main distance calculation methods include: shortest distance method, longest distance method, middle distance method, center of gravity method, dispersion sum of squares method and class average distance method, and these distance methods include the Euclidean distance, Mahalanobis distance, cosine similarity, etc.

It is mainly a process in which the distance between the sample values is calculated, and then the samples with the smallest distance value are combined. The specific steps are as follows:

1. Define how to calculate the distance between sample data.

2. Calculate the distance between the two pairs of the initial sample to form a distance matrix.

3. Filter out the minimum distance value in the distance matrix, and merge the two samples corresponding to the minimum value into a new sample.

4. Incorporate the new sample into the sample, iteratively calculate the distance matrix again, and repeat the step until all the samples are merged into one large sample.

The distance between the centers of two clusters is defined as the distance between the centers of gravity of the two classes, and the center of gravity of the class is the average of the samples that belong to that class. The concept of center of gravity is a good representation of the properties of the class.

The method of clustering data using the class averaging method is a dynamic clustering method, also known as the stepwise clustering method, in which the general step is to classify the samples in a coarse-grained way, and then gradually adjust the clusters to which the samples belong until all the samples are divided into reasonable clusters.

Hello, to put it simply, categorization or classification is to label objects according to some standard, and then classify them according to the label.

To put it simply, clustering refers to the process of finding out the cause of clustering between things through some kind of clumping analysis without "labels" in advance.

The difference is that the classification is a predefined category, and the number of categories remains the same. The classifier needs to be trained by the classification training corpus of manual annotation, which belongs to the category of guided learning. Clustering, on the other hand, does not have predetermined categories, and the number of categories is indefinite.

Clustering does not require manual labeling and pre-trained classifiers, and categories are automatically generated during the clustering process. Classification is suitable for situations where the category or classification system has been determined, such as classifying books according to the national map classification; Clustering is suitable for situations where there is no classification system and the number of categories is uncertain, and is generally used as the front end of some applications, such as multi-document summaries, post-search engine clustering (meta-search), etc.

The purpose of classification is to learn a classification function or classification model (also often called a classifier) that maps data items in a database to a class in a given category. To construct a classifier, you need to have a training sample dataset as input. The training set consists of a set of database records or tuples, each tuple is a feature vector consisting of the values of the field in question (also known as an attribute or feature), and the training samples have a category tag.

The form of a concrete sample can be expressed as: (v1,v2,..vn; c)；where vi represents the field value and c represents the category.

Classifiers are constructed using statistical methods, machine learning methods, neural network methods, and so on.

Clustering refers to the process of clustering samples without categories into different groups according to the principle of "clustering of things by like", and the collection of such a group of data objects is called cluster, and each such cluster is described. Its purpose is such that samples belonging to the same cluster should be similar to each other, and samples from different clusters should be dissimilar enough. Unlike classification rules, clustering does not know how many groups and what kind of groups will be divided into them, nor what spatial discrimination rules will be used to define groups.

The purpose of this study is to discover the functional relationships between the properties of spatial entities, and to express the knowledge mined in mathematical equations with properties called variables. Clustering technology is booming, covering data mining, statistics, machine learning, spatial database technology, biology, marketing and other fields, and cluster analysis has become a very active research topic in the field of data mining research. Common clustering algorithms include:

K-means clustering algorithm, K-center point clustering algorithm, clarans, birch, clique, dbscan, etc.

This depends on the specific clustering algorithm, and different algorithms have different data requirements. For example, the k-means algorithm requires:

Data type, classification attributes are not applicable.

Sample distribution: not suitable for non-convex shapes, data distribution: sensitive to noise and outliers.

There are certain requirements for clustering, and the typical requirements for clustering are as follows:

Scalability. The ability to handle different types of attributes.

Discover clusters of arbitrary shapes.

The domain knowledge used to determine the input parameters is minimized.

Ability to process noisy data.